Remote Sensing with UAV and Mobile Recharging Vehicle Rendezvous

Abstract

Small unmanned aerial vehicles (UAVs) equipped with sensors offer an effective way to perform high-resolution environmental monitoring in remote areas but suffer from limited battery life. In order to perform large-scale remote sensing, a UAV must cover the area using multiple discharge cycles. A practical and efficient method to achieve full coverage is for the sensing UAV to rendezvous with a mobile recharge vehicle (MRV) for a battery exchange, which is an NP-hard problem. Existing works tackle this problem using slow genetic algorithms or greedy heuristics. We propose an alternative approach: a two-stage algorithm that iterates between dividing a region into independent subregions aligned to MRV travel and a new diffusion heuristic that performs a local exchange of points of interest between neighboring subregions. The algorithm outperforms existing state-of-the-art planners for remote sensing applications, creating more fuel efficient paths that better align with MRV travel.